Sudan Jha1, Gyanendra Prasad Joshi2, Lewis Nkenyereya3, Dae Wan Kim4, *, Florentin Smarandache5
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1203-1220, 2020, DOI:10.32604/cmc.2020.011618
Abstract Raw data are classified using clustering techniques in a reasonable manner to
create disjoint clusters. A lot of clustering algorithms based on specific parameters have
been proposed to access a high volume of datasets. This paper focuses on cluster analysis
based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based
clustering technique. This algorithm addresses the shortcomings of the k-means clustering
algorithm by overcoming the limitations of the threshold-based clustering algorithm. To
evaluate the validity of the proposed method, several validity measures and validity indices
are applied to the Iris dataset (from the University of California, Irvine, Machine… More >
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